Attention-based motion forecasting models: Attention is used widely in motion forecasting models to fuse diverse features, especially agent and lane features. [1]} proposed a novel form of graph convolution to aggregate short and long-range lane features and used self and cross attention to fuse agent and lane features. [2]}, [3]}, [4]} also use self and cross-attention between agent and lane features. More recently, several transformer-based papers such as [5]}, [6]}, [7]} incorporate temporal attention as well. Some works, such as [8]}, introduce a novel formulation of attention to enforce spatial and rotational invariance. Other works, such as [9]}, model the scene using two graph layers and use attention to fuse embeddings of the two layers.
In our experiments, we applied our proposed cycle loss method to two attention-based motion forecasting models, GOHOME [3]} and Autobots [6]}. We selected them because of their popularity and competitive performance.
